A sequence of medical images representing three-dimensional objects, such as a set of magnetic resonance imaging (MRI) or computed tomography (CT) scans are often segmented to form a surface point set representation of an organ, bone, or other object of interest. This point set often contains a very large number of points and significant noise or unwanted data is present. Available software can clean up the data and produce a surface triangularization; however, this often results in a very large number of polygons being created.
In order to perform analysis of the object, such as, for example, Finite Element Analysis (FEA), a full volume mesh must be generated, and, for FEA, the mesh will also have certain restrictions applicable to FEA. The process to convert from raw scans to an analysis-suitable representation can involve dozens of hours of tedious manual manipulations by a user. Further, if one desires to perform “what-if” analyses for different possible scenarios (e.g. possible surgeries) the geometry may be tedious to modify. Final model geometries contain a numerous amount of data, but not much information. Specially trained people must examine the images to extract the information desired (e.g. disease detection).
In certain applications, such as epidemiological studies, a large number of models, perhaps hundreds or thousands, must be processed. The tedious, human-intensive process of manually modifying the surface meshing arising from the discrete point set makes such studies impractical.